Adaptive and Balanced Re-initialization for Long-timescale Continual Test-time Domain Adaptation

• URL: http://arxiv.org/abs/2602.06328v1
• Date: Fri, 06 Feb 2026 02:53:15 GMT
• Title: Adaptive and Balanced Re-initialization for Long-timescale Continual Test-time Domain Adaptation
• Abstract summary: Continual test-time domain adaptation aims to adjust models so that they can perform well over time across non-stationary environments.<n>Can the model adapt to continually changing environments over a long time?<n>We propose Adaptive-and-Balanced Re-initialization (ABR) towards preserving the model's long-term performance.
• Score: 27.967679954595123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual test-time domain adaptation (CTTA) aims to adjust models so that they can perform well over time across non-stationary environments. While previous methods have made considerable efforts to optimize the adaptation process, a crucial question remains: Can the model adapt to continually changing environments over a long time? In this work, we explore facilitating better CTTA in the long run using a re-initialization (or reset) based method. First, we observe that the long-term performance is associated with the trajectory pattern in label flip. Based on this observed correlation, we propose a simple yet effective policy, Adaptive-and-Balanced Re-initialization (ABR), towards preserving the model's long-term performance. In particular, ABR performs weight re-initialization using adaptive intervals. The adaptive interval is determined based on the change in label flip. The proposed method is validated on extensive CTTA benchmarks, achieving superior performance.

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